Title:
A Rao-Blackwellized Particle Filter for Topological Mapping
A Rao-Blackwellized Particle Filter for Topological Mapping
Author(s)
Ranganathan, Ananth
Dellaert, Frank
Dellaert, Frank
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Abstract
We present a particle filtering algorithm to construct
topological maps of an uninstrument environment. The
algorithm presented here constructs the posterior on the space
of all possible topologies given measurements, and is based on our
previous work on a Bayesian inference framework for topological
maps [21]. Constructing the posterior solves the perceptual
aliasing problem in a general, robust manner. The use of a
Rao-Blackwellized Particle Filter (RBPF) for this purpose makes
the inference in the space of topologies incremental and run in
real-time. The RBPF maintains the joint posterior on topological
maps and locations of landmarks. We demonstrate that, using the
landmark locations thus obtained, the global metric map can be
obtained from the topological map generated by our algorithm
through a simple post-processing step. A data-driven proposal
is provided to overcome the degeneracy problem inherent in
particle filters. The use of a Dirichlet process prior on landmark
labels is also a novel aspect of this work. We use laser range scan
and odometry measurements to present experimental results on
a robot.
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Date Issued
2006-05
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Text
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